Sarmentol H derived from Sedum sarmentosum Bunge directly targets FXR to mitigate cholestasis by recruiting SRC-1.

BACKGROUND: Farnesoid X receptor (FXR) is a vital receptor for bile acids and plays an important role in the treatment of cholestatic liver disease. In addition to traditional bile acid-based steroidal agonists, synthetic alkaloids are the most commonly reported non-steroidal FXR agonists. Sarmentol H is a nor-sesquiterpenoid obtained from Sedum sarmentosum Bunge, and in vitro screening experiments have shown that it might be related to the regulation of the FXR pathway in a previous study. PURPOSE: To investigate the therapeutic effects of sarmentol H on cholestasis and to determine whether sarmentol H directly targets FXR to mitigate cholestasis. Furthermore, this study aimed to explore the key amino acid residues involved in the binding of sarmentol H to FXR through site-directed mutagenesis. METHODS: An intrahepatic cholestasis mouse model was established to investigate the therapeutic effects of sarmentol H on cholestasis. In vitro experiments, including Co-Ip and FXR-EcRE-Luc assays, were performed to assess whether sarmentol H activates FXR by recruiting the receptor coactivator SRC1. CETSA, SIP, DARTS, and ITC were used to determine the binding of sarmentol H to FXR protein. The key amino acid residues for sarmentol H binding to FXR were analyzed by molecular docking and site-directed mutagenesis. Finally, we conducted in vivo experiments on wild-type and Fxr-/- mice to further validate the anticholestatic target of sarmentol H. RESULTS: Sarmentol H had significant ameliorative effects on the pathological conditions of cholestatic mice induced with ANIT. In vitro experiments suggested that it is capable of activating FXR and regulating downstream signaling pathways by recruiting SRC1. The target validation experiments showed that sarmentol H had the ability to bind to FXR as a ligand (KD = 2.55 µmol/L) and enhance the stability of its spatial structure. Moreover, site-directed mutagenesis revealed that THR292 and TYR365 were key binding sites for sarmentol H and FXR. Furthermore, knockout of the Fxr gene resulted in a significantly higher degree of ANIT-induced cholestatic liver injury than that in wild-type cholestatic mice, and the amelioration of cholestasis or regulatory effects on FXR downstream genes by sarmentol H also disappeared in Fxr-/- cholestatic mice. CONCLUSION: Sarmentol H is an FXR agonist. This is the first study to show that it exerts a significant therapeutic effect on cholestatic mice, and can directly bind to FXR and activate it by recruiting the coactivator SRC1.

Machine Learning Models for Parkinson Disease: Systematic Review.

Background: With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems. Objective: To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression. Methods: We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: "Parkinson's" AND ("ML" OR "prediction" OR "classification" OR "detection" or "artificial intelligence" OR "AI"). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms. Results: Only 65.5% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results. Conclusions: This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.

Identification and quantification of tire wear particles by employing different cross-validation techniques: FTIR-ATR Micro-FTIR, Pyr-GC/MS, and SEM.

Tire wear particles (TWPs) are one of the environment's most important emission sources of microplastics. In this work, chemical identification of these particles was carried out in highway stormwater runoff through cross-validation techniques for the first time. Optimization of a pre-treatment method (i.e., extraction and purification) was provided to extract TWPs, avoiding their degradation and denaturation, to prevent getting low recognizable identification and consequently underestimates in the quantification. Specific markers were used for TWPs identification comparing real stormwater samples and reference materials via FTIR-ATR, Micro-FTIR, and Pyrolysis-gas-chromatography-mass spectrometry (Pyr-GC/MS). Quantification of TWPs was carried out via Micro-FTIR (microscopic counting); the abundance ranged from 220,371 ± 651 TWPs/L to 358,915 ± 831 TWPs/L, while the higher mass was 39,6 ± 9 mg TWPs/L and the lowest 31,0 ± 8 mg TWPs/L. Most of the TWPs analyzed were less than 100 µm in size. The sizes were also confirmed using a scanning electron microscope (SEM), including the presence of potential nano TWPs in the samples. Elemental analysis via SEM supported that a complex mixture of heterogeneous composition characterizes these particles by agglomerating organic and inorganic particles that could derive from brake and road wear, road pavement, road dust, asphalts, and construction road work. Due to the analytical lack of knowledge about TWPs chemical identification and quantification in scientific literature, this study significantly contributes to providing a novel pre-treatment and analytical methodology for these emerging contaminants in highway stormwater runoff. The results of this study highlight the uttermost necessity to employ cross-validation techniques, i.e., FTIR-ATR, Micro-FTIR, Pyr-GC/MS, and SEM for the TWPs identification and quantification in the real environmental samples.

Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions.

The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions ("leave-one-out CV" accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.

Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study.

The aim of this pilot study was to develop logistic regression (LR) and support vector machine (SVM) models that differentiate low from high risk for prolonged hospital length of stay (LOS) in a South African cohort of 383 colorectal cancer patients who underwent surgical resection with curative intent. Additionally, the impact of 10-fold cross-validation (CV), Monte Carlo CV, and bootstrap internal validation methods on the performance of the two models was evaluated. The median LOS was 9 days, and prolonged LOS was defined as greater than 9 days post-operation. Preoperative factors associated with prolonged LOS were a prior history of hypertension and an Eastern Cooperative Oncology Group score between 2 and 4. Postoperative factors related to prolonged LOS were the need for a stoma as part of the surgical procedure and the development of post-surgical complications. The risk of prolonged LOS was higher in male patients and in any patient with lower preoperative hemoglobin. The highest area under the receiving operating characteristics (AU-ROC) was achieved using LR of 0.823 (CI = 0.798-0.849) and SVM of 0.821 (CI = 0.776-0.825), with each model using the Monte Carlo CV method for internal validation. However, bootstrapping resulted in models with slightly lower variability. We found no significant difference between the models across the three internal validation methods. The LR and SVM algorithms used in this study required incorporating important features for optimal hospital LOS predictions. The factors identified in this study, especially postoperative complications, can be employed as a simple and quick test clinicians may flag a patient at risk of prolonged LOS.